Modeling and Prescribed Performance Control for Cold Rolling Mill HAGC System with Measurement Delay
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- 英文论文题名：Modeling and Prescribed Performance Control for Cold Rolling Mill HAGC System with Measurement Delay
- 论文作者：LI De ; LIU Guopin ; HUA Changchun ; GUAN Xinping
- 英文论文作者：LI De ; LIU Guopin ; HUA Changchun ; GUAN Xinping ; Institute of Electrical Engineering ; Yanshan University ; Department of Automation ; Shanghai Jiao Tong University
- 年：2017
- 作者机构：Institute of Electrical Engineering,Yanshan University;Department of Automation,Shanghai Jiao Tong University;
- 英文论文关键词：HAGC system ; prescribed performance control ; time-delay ; adaptive neural network
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TG334.9
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：267k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, the modeling and prescribed performance control(PPC) problem is investigated for cold rolling mill system. First, considering the measurement delay and disturbances in practical process, a mathematical model is established for the cold rolling mill hydraulic automatic gauge control(HAGC) system. Then, based on a state transformation with the performance index function, a predefined performance control scheme is proposed to guarantee both transient-state and steadystate performances. In addition, adaptive neural networks approach is employed to approximate the uncertainties in the controller design procedure. It is proved that the proposed controller can guarantee the stability of the closed-loop system in the sense of uniformly ultimately boundedness. Finally, simulation results illustrate the effectiveness of the proposed method.
In this paper, the modeling and prescribed performance control(PPC) problem is investigated for cold rolling mill system. First, considering the measurement delay and disturbances in practical process, a mathematical model is established for the cold rolling mill hydraulic automatic gauge control(HAGC) system. Then, based on a state transformation with the performance index function, a predefined performance control scheme is proposed to guarantee both transient-state and steadystate performances. In addition, adaptive neural networks approach is employed to approximate the uncertainties in the controller design procedure. It is proved that the proposed controller can guarantee the stability of the closed-loop system in the sense of uniformly ultimately boundedness. Finally, simulation results illustrate the effectiveness of the proposed method.
In this paper, the modeling and prescribed performance control(PPC) problem is investigated for cold rolling mill system. First, considering the measurement delay and disturbances in practical process, a mathematical model is established for the cold rolling mill hydraulic automatic gauge control(HAGC) system. Then, based on a state transformation with the performance index function, a predefined performance control scheme is proposed to guarantee both transient-state and steadystate performances. In addition, adaptive neural networks approach is employed to approximate the uncertainties in the controller design procedure. It is proved that the proposed controller can guarantee the stability of the closed-loop system in the sense of uniformly ultimately boundedness. Finally, simulation results illustrate the effectiveness of the proposed method.
引文
[1]Fang,Y.M.,Jiao,X.H.,&Wang,Y.(2000).Pole assignment self tuning control and its application in gauge control system for cold strip mill.Control Theory&Applications,17(2),240-248.
[2]Yang,J.M.,Liu,Y.,&Xu,Y.(2009).Application of neural network adaptive control to the AGC system in cold rolling mill.Metallurgical Equipment,2(2),14-17.
[3]Sun,J.L.,Peng,Y.,&Liu,H.(2009).Dynamic modeling of gauge control system based on thickness gauge and design of H∞robust controller.Journal of Mechanical Engineering,45(6),160-170.
[4]Bekiaris-Liberis,N.,&Krstic,M.(2016).Compensation of infinite-dimensional actuator and Sensor dynamics nonlinear and delay-adaptive systems.IEEE Control Systems Magazine2010;30(1):22-41.
[5]Zhu,Q.,Zhang,T.P.,&Fei,S.(2010).Adaptive tracking control for input delayed MIMO nonlinear systems.Neurocomputing,74,472-480.
[6]Selivanov,A.,Fridman,E.,&Fradkov,A.(2015).Passification-based adaptive control:Uncertain input and output delays.Automatica,54,107-113.
[7]Bechlioulis,C.,&Rovithakis,A.(2009).Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems.Automatica,45(2),532-538.
[8]Bechlioulis,C.,&Rovithakis,A.(2009).Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance.IEEE transactions on Automatic Control,53(9),2090-2099.
[9]Hua,C.C.,Zhang,L.L.,&Guan,X.P.(2014).Output feedback control for interconnected time-delay systems with prescribed performance.Neurocomputing,129(4),208-215.
[10]Liu,Y.J.,Tong,S.C.,Li,T.S.,&Chen.C.L.(2011).Adaptive neural output feedback controller design with reducedorder observer for a class of uncertain nonlinear SISO systems.IEEE transactions on Neural Networks,22(8),1328-1334.
[11]Jiang,B.,Shen,Q.K.,&Shi,P.(2015).Neural-networked adaptive tracking control for switched nonlinear purefeedback systems under arbitrary switching.Automatica,61(11),119-125.
[12]Pai,M.C.(2015).Dynamic output feedback RBF neural network sliding mode control for robust tracking and model following.Nonlinear Dynamics,79(2),1023-1033.
[13]Gao,Y.,Wang,Y.Q.,&Kong,X.D.(1998).The study on the dynamic model of hydraulic AGC system in a strip mill.Journal Of Yanshan University,.22(3),259-262.
[2]Yang,J.M.,Liu,Y.,&Xu,Y.(2009).Application of neural network adaptive control to the AGC system in cold rolling mill.Metallurgical Equipment,2(2),14-17.
[3]Sun,J.L.,Peng,Y.,&Liu,H.(2009).Dynamic modeling of gauge control system based on thickness gauge and design of H∞robust controller.Journal of Mechanical Engineering,45(6),160-170.
[4]Bekiaris-Liberis,N.,&Krstic,M.(2016).Compensation of infinite-dimensional actuator and Sensor dynamics nonlinear and delay-adaptive systems.IEEE Control Systems Magazine2010;30(1):22-41.
[5]Zhu,Q.,Zhang,T.P.,&Fei,S.(2010).Adaptive tracking control for input delayed MIMO nonlinear systems.Neurocomputing,74,472-480.
[6]Selivanov,A.,Fridman,E.,&Fradkov,A.(2015).Passification-based adaptive control:Uncertain input and output delays.Automatica,54,107-113.
[7]Bechlioulis,C.,&Rovithakis,A.(2009).Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems.Automatica,45(2),532-538.
[8]Bechlioulis,C.,&Rovithakis,A.(2009).Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance.IEEE transactions on Automatic Control,53(9),2090-2099.
[9]Hua,C.C.,Zhang,L.L.,&Guan,X.P.(2014).Output feedback control for interconnected time-delay systems with prescribed performance.Neurocomputing,129(4),208-215.
[10]Liu,Y.J.,Tong,S.C.,Li,T.S.,&Chen.C.L.(2011).Adaptive neural output feedback controller design with reducedorder observer for a class of uncertain nonlinear SISO systems.IEEE transactions on Neural Networks,22(8),1328-1334.
[11]Jiang,B.,Shen,Q.K.,&Shi,P.(2015).Neural-networked adaptive tracking control for switched nonlinear purefeedback systems under arbitrary switching.Automatica,61(11),119-125.
[12]Pai,M.C.(2015).Dynamic output feedback RBF neural network sliding mode control for robust tracking and model following.Nonlinear Dynamics,79(2),1023-1033.
[13]Gao,Y.,Wang,Y.Q.,&Kong,X.D.(1998).The study on the dynamic model of hydraulic AGC system in a strip mill.Journal Of Yanshan University,.22(3),259-262.